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Model-Based Reasoning in Scientific Discovery

herausgegeben von: Lorenzo Magnani, Nancy J. Nersessian, Paul Thagard

Verlag: Springer US

Enthalten in: Professional Book Archive

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The volume is based on the papers that were presented at the Interna tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de ductive and inductive arguments. Understanding the contribution of model ing practices to discovery and conceptual change in science requires ex panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model based reasoning to be considered in this book. The models are intended as in terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.

Inhaltsverzeichnis

Frontmatter

Models, Mental models, and Representations

Frontmatter

Model-Based Reasoning in Conceptual Change

Abstract

This paper addresses how specific modeling practices employed by scientists are productive methods of conceptual change in science. Within philosophy, where the identification of reasoning with argument and logic is deeply ingrained, these practices have not traditionally been considered significant forms of scientific reasoning. Embracing these modeling practices as “methods” of conceptual change in science requires expanding philosophical notions of scientific reasoning to encompass forms of creative reasoning. I focus on three forms of model-based reasoning demonstrated in my previous work as generative of conceptual change in science: analogical modeling, visual modeling, and thought experimenting. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. In the modeling process, various forms of abstraction, such as limiting case, idealization, generalization, generic modeling, are utilized. Evaluation and adaptation take place in light of structural, causal, and/or functional constraint satisfaction. Simulation can be used to produce new states and enable evaluation of behaviors, constraint satisfaction, and other factors.

Nancy J. Nersessian

Tracing the Development of Models in the Philosophy of Science

Abstract

An overview is provided of how the concept of a scientific model has devel-oped and changed in the philosophy of science in the course of this Century. I identify three shifts of interest in the treatment of the topic of scientific models. First, only from the 1950s did models begin to be considered relevant to the scientific enterprise, motivated by the desire to account for issues such as theory change and creativity in scientific discovery. Second, I examine how philosophers then increasingly concentrated on the analysis of the functions of models, e.g. for explanation or for guiding and suggesting new experiments. Finally, I show how an analysis of the functions of models could lead to the consideration of their function not just within science, but also in human cognition, so that models are now sometimes viewed as tools of actual (rather than logically reconstructed) scientific thinking.

Daniela M. Bailer-Jones

Using Models to Represent Reality

Abstract

In this paper I develop a unified interpretation of the nature and role of models in science. Central to this interpretation is an understanding of the relationships between models and other elements of an understanding of science, particularly theories, data, and analogy. I begin by criticizing a standard interpretive or instantial view of models, derived from mathematical logic, as not being adequate for empirical science. I then go on to develop a representational view of models which, I argue, is much more adequate to the needs of empirical science. I conclude that scientific reasoning is to a large extent model-based reasoning. It is models almost all the way up and models almost all the way down.

Ronald N. Giere

Models and Diagrams within the Cognitive Field

Abstract

My objective in this paper is to argue that research on modeling within science and technology should be cautious about approaching models in isolation but should regard them as part of a complex, generative field. While there certainly may be rhetorical purposes for isolating a particular model to bring into relief a particular “way of seeing” in the history of science, caution is essential if such a presentation of models doesn’t become more a caricature of science. The plethora of books representing the history of science through a series of icons or great figures is an example. Others examples can be found in the misleading assimilation of Kuhn’s idea of paradigms (Hoyningen-Huene,1993;Nersessian,1998).After making several brief comments about model-based reasoning, I will notice what might he learned from reading about the generative cognitive field in which models emerge. I will conclude my comments by making several comments on the relation between models and diagrams.

Kenneth J. Knoespel

Theories,Models,and Representations

Abstract

I argue against an account of scientific representation suggested by the semantic, or structuralist, conception of scientific theories. Proponents of this conception often employ the term “model” to refer to bare “structures”, which naturally leads them to attempt to characterize the relation between models and reality as a purely structural one. I argue instead that scientific models are typically “representations”, in the pragmatist sense of the term: they are inherently intended for specific phenomena. Therefore in general scientific models are not (merely) structures. I then explore some consequences of this pragmatist account of representation, and argue that it sheds light upon the distinction between theories and models. I finish by briefly addressing some critical comments due to Bas Van Fraassen.

Mauricio Suárez

How Scientists Build Models In Vivo Science as a Window on the Scientific Mind

Abstract

How do scientists think, reason, and generate new models and theories? Over the past decade, I have been addressing these questions by investigating scientists in their own labs, reasoning about their research “live’ and by conducting experiments on scientific thinking and model building. The labs are molecular biology and immunology laboratories in the U.S., Canada, and Italy. I have found that one place where much reasoning and new discoveries are made is at weekly lab meetings. We have performed extensive cognitive analyses of these meetings and have identified some of the key components of contemporary scientific thinking that are important in generating new models, modifying old models and solving difficult problems. In this paper I will outline four important activities that are important in model building: analogical reasoning, attention to unexpected findings, experimental design, and distributed reasoning.

Kevin Dunbar

Discovery Processes and Mechanisms

Frontmatter

A Simulation of Model-Based Reasoning about Disparate Phenomena

Abstract

This paper argues that an adequate theory of discovery must bring the formal approach of computer science and cognitive psychology closer to the empirical approach of history and the social studies of science. We argue that a computer simulation is analogous to a theory of scientific investigation in that it is comprised of a number of models of activities and entities. We describe a model of experimentation to illustrate an approach to discovery simulation that shows how formal and empirical approaches may be integrated.

David C. Gooding, Tom R. Addis

Scientific Discovery and Technological Innovation: Ulcers, Dinosaur Extinction, and the Programming Language Java

Abstract

Whereas scientists formulate laws and theories to account for observations, inventors create new technology to accomplish practical goals. Scientific discovery and technological innovation are among the most important accomplishments of the creative human mind. The aim of this paper is to compare how scientists produce discoveries with how inventors produce new technology. After briefly reviewing an account of the recent discovery of the bacterial theory of ulcers, we show that a similar account applies to the discovery that dinosaurs became extinct because of an asteroid collision. Both these discoveries involved a combination of serendipity, questioning and search. We then describe how these three processes also contributed to a very important recent technological innovation, the development of the programming language Java. The paper concludes with a more general assessment of the similarities and differences between cognitive processes involved in discovery and invention.

Paul Thagard, David Croft

A Hierarchy of Models and Electron Microscopy

Abstract

This paper examines a hierarchical system of models connecting data and theory that was proposed by Patrick Suppes, and later developed by Deborah Mayo. After it summarizes examples found in Suppes and Mayo it takes a closer look at the experimental model and concludes that the experimental model has an evaluative role in the hierarchy, as well as a representational role. The last section of the paper illustrates with the example of electron microscopy and presents some problems that require further study.

Todd Harris

Expansion and Justification of Models: the Exemplary Case of Galileo Galilei

Abstract

For T.S. Kuhn in his Postscript to Scientific Revolutions and R. Giere recently science is as a cluster of models that is produced and expands from a few pro-typical cases. Both, they are against any seeking of common characteristics that could function as “necessary and sufficient conditions” of membership in the cluster, leaving the scientific community as the final judge of what constitutes a problem for a specific science. With a sketchy examination of Galileo’s work on motion I try to show that two models could belong to same cluster, not because they share common characteristics, but because the one is a limiting case of the other. In this case, we can have rules of transformation that although are not logical are nevertheless “objective” and rigorous. Moreover, such a context reframe the problem of when a model is a good representation of a physical system.

Vasilis Raisis

Simplifying Bayesian Inference: The General Case

Abstract

We present empirical evidence that human reasoning follows the rules of probability theory, if information is presented in “natural formats”. Human reasoning has often been evaluated in terms of humans’ ability to deal with probabilities. Yet, in nature we do not observe probabilities, we rather count samples and their subsets. Our concept of Markov frequencies generalizes Gigerenzer and Hoffrage’s “natural frequencies”, which are known to foster insight in Bayesian situations with one cue. Markov frequencies allow to visualize Bayesian inference problems even with an arbitrary number of cues.

Stefan Krauß, Laura Martignon, Ulrich Hoffrage

Complexity versus Complex Systems: A New Approach to Scientific Discovery

Abstract

Extraction of quantitative features from observations via measuring devices M means that the words of science are coded as numbers, and the syntaxis is a set of mathematical rules, thus all consequences should be worked in a purely deductive way. This characteristic of science displays two orders of drawbacks, namely, undecidability of deductive procedures, and intractability of complex situations. The way out of such a crisis consists in a frequent readjustment of M suggested by the observed events. This adaptive strategy differs from the adaptivity of a learning machine, which — inputted by a data stream — readjusts itself over a class of theoretical explanations in order to select the optimal choice. On the contrary, the scientist not only modifies the explanations for a fixed data set, but also explores different data sets by modifying M, that is, by selecting a different point of view. This M-adjustment is a pre-linguistic operation, not expressible by a formal language. Hence, the scientific endeavor can not be reduced to a machine task.

F. Tito Arecchi

Creative Inferences and Abduction

Frontmatter

Model-Based Reasoning in Creative Processes

Abstract

Combining a contextual approach to problem solving with results on some recently developed (non-standard) logics, I present in this paper a general frame for the methodological study of model-based reasoning in creative processes. I argue that model-based reasoning does not require that we turn away from logic. I also argue, however, that in order to better understand and evaluate creative processes that involve model-based reasoning, and in order to formulate guidelines for them, we urgently need to extend the existing variety of logics.

Joke Meheus

Model-Based Creative Abduction

Abstract

My contribution aims to introduce the distinction, not previously analyzed, between two kinds of abduction,theoretical and manipulative, in order to provide an integrated framework to explain some of the main aspects of both creative and model-based reasoning effects engendered by the practice of science. The distinction appears to be extremely convenient: creativity will be viewed as the result of the highest cases of theoretical abduction showing the role of the so-called model-based abduction. Moreover, I will delineate the first features of what I call manipulative abduction showing how we can find methods of constructivity at the experimental stage, where the recent epistemological tradition has settled the most negative effects of theory-ladenness.

Lorenzo Magnani

Abduction and Geometrical Analysis. Notes on Charles S. Peirce and Edgar Allan Poe

Abstract

The method of analysis and synthesis in Greek geometry is perhaps the most significant key idea in the history of heuristic reasoning. In theoretical analysis, reasoning goes backward from a theorem to the axioms from which it deductively follows. In problematical analysis, the desired thing is supposed to be given (this defines the so-called “model figure”), and then the reasoning again goes backward looking for possible constructions from which the sought thing results. This paper states that Charles S. Peirce’s description of abduction, as a retroductive inference of a cause from its effects, is an instance of the propositional interpretation of analysis. What Jaakko Hintikka calls the “analysis of figures” view of problematical analysis is illustrated by detective stories of Edgar Allan Poe. The same idea is repeated in Poe’s essay “Philosophy of Composition” where he tells that he wrote his poem The Raven (1845) analytically by starting at the end.

Ilkka Niiniluoto

The Hierarchy of Models in Simulation

Abstract

This paper focuses on the role of the computer in helping to manage mathe-matically unsolvable sets of equations that arise within models in the physical sciences. The study of these models often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior of these systems. I argue that this process of transformation, which involves a hierarchy of models, is also a process of knowledge creation, and it has its own epistemology. Accordingly, I urge philosophers of science to examine more carefully the process of theory articulation, the process by which a general theory is made to conform to a particular application. It is a relatively neglected aspect of scientific practice, but it plays a role that is often as crucial, as complex, and as creative as theorizing and experimenting. Indeed, my conclusion will be that we now need a new philosophy of simulation to complement recent work on the philosophy of experiment.

Eric Winsberg

Abducting Explanation

Abstract

This paper examines the nature of three well-known but distinct discovery engines: Inductive discovery (ID), Inference to the best explanation (IBE) and Abductive discovery (AD). Any discovery process requires modifications in the background knowledge: these belief revisions take place in four different dimensions. The three discovery engines will be compared with respect to these dimensions. Special attention will paid to (AD) since it constitutes the most troublesome of the three.

Vincent F. Hendricks, Jan Faye

Fictionalism and the Logic of “As If” Conditionals

Abstract

The paper accepts Hans Vaihinger’s suggestion that “as if’ conditionals contain a counterfactual element and aims to developing a formal analysis of them by using conditionals expressing a consequential connection between the clauses. The key idea is that ”as if’ counterfactuals are explicit counterfactuals with true consequents, whose rendering is unproblematic at the propositional level. The most interesting problems however arise when the “as if’ concerns not a comparison between facts but a comparison between gradable predicates. The solution proposed here relies on Seuren’s theory of comparison and make an essential use of existential quantification over extents, while existential quantification over predicates allows to render the notion of similarity which appears to be involved in many ordinary uses of “as if”. It is stressed that the proposed analysis gives a solution also to the problem of so-called counter-comparatives, which has been treated by David Lewis in the framework of counterpart theory.

Claudio Pizzi

Scientific Modeling: A Multilevel Feedback Process

Abstract

Model construction is one of the key scientific activities. In distinction to the majority of the previous machine discovery systems, model formation applies in theory-rich context. Our long-term goal is automation of model construction. This paper reports on exploratory work towards that goal. We start from the distinction between models and theories, which is critical to the presented approach. We also distinguish between modeling and two scientific activities, which are different but which support modeling: construction of operational definitions and experimentation. Then we present the basic steps of scientific model construction, outlining data structures and an algorithm which, using a number of feedback loops, incrementally develops a model of a natural phenomenon. A walk through example is used to present the algorithm: motion of a cylinder that rolls downwards on an inclined plane.

Jan M. Zytkow

Backmatter

Titel: Model-Based Reasoning in Scientific Discovery
herausgegeben von: Lorenzo Magnani
Nancy J. Nersessian
Paul Thagard
Verlag: Springer US
Electronic ISBN: 978-1-4615-4813-3
Print ISBN: 978-1-4613-7181-6
DOI: https://doi.org/10.1007/978-1-4615-4813-3

Springer Professional

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

Models, Mental models, and Representations

Frontmatter

Model-Based Reasoning in Conceptual Change

Tracing the Development of Models in the Philosophy of Science

Using Models to Represent Reality

Models and Diagrams within the Cognitive Field

Theories,Models,and Representations

How Scientists Build Models In Vivo Science as a Window on the Scientific Mind

Discovery Processes and Mechanisms

Frontmatter

A Simulation of Model-Based Reasoning about Disparate Phenomena

Scientific Discovery and Technological Innovation: Ulcers, Dinosaur Extinction, and the Programming Language Java

A Hierarchy of Models and Electron Microscopy

Expansion and Justification of Models: the Exemplary Case of Galileo Galilei

Simplifying Bayesian Inference: The General Case

Complexity versus Complex Systems: A New Approach to Scientific Discovery

Creative Inferences and Abduction

Frontmatter

Model-Based Reasoning in Creative Processes

Model-Based Creative Abduction

Abduction and Geometrical Analysis. Notes on Charles S. Peirce and Edgar Allan Poe

The Hierarchy of Models in Simulation

Abducting Explanation

Fictionalism and the Logic of “As If” Conditionals

Scientific Modeling: A Multilevel Feedback Process

Backmatter